Different neural underpinnings for self and prosocial decision-making under cognitive load
Bibliographic record
Abstract
The data acquisition was conducted using a Siemens Prisma 3.0 T MRI machine. Functional volumes were obtained through multiple slice T2-weighted echo planar imaging (EPI) sequences, utilizing the following parameters: repetition time of 1500 ms, echo time of 30 ms, flip angle of 75°, field of view measuring 192 × 192 mm2, 72 slices covering the entire brain, slice thickness of 2 mm, and voxel size of 2 × 2 × 2 mm3. Preprocessing of fMRI data was performed using SPM12 (Wellcome Department of Imaging Neurosciences, University College London, U.K.) in the MATLAB 2020b (The MathWorks Inc). The images underwent slice timing correction, motion correction, coregistration and normalization to Montreal Neurological Institute (MNI) space with a spatial resolution of 2 × 2 × 2 mm3, and smoothing with an isotropic Gaussian kernel of 6 mm. Moreover, the fMRI data was high-pass filtered at a cutoff of 128 Hz.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".